Medical Imaging Technology
Online ISSN : 2185-3193
Print ISSN : 0288-450X
ISSN-L : 0288-450X
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Volume 31, Issue 5
Displaying 1-16 of 16 articles from this issue
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Computational Anatomy—Frontiers of Mathematical Foundation, Application Systems and Clinical Applications of Medical Imaging
  • Computational Anatomy-Frontiers of Mathematical Foundation,Application Systems and Clinical Applications of Medical Imaging: Introduction
    Kensaku MORI
    2013 Volume 31 Issue 5 Pages 261-262
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.261
    JOURNAL FREE ACCESS
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  • Mathematical Models for Anatomical Landmarks and Point Distribution Representation of Anatomical Objects for Medical Image Understanding
    Yoshitaka MASUTANI, Hidekata HONTANI, Atsushi IMIYA
    2013 Volume 31 Issue 5 Pages 263-269
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.263
    JOURNAL FREE ACCESS
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    One of the important purposes of the new discipline “Computational Anatomy” is robust computational understanding of clinical images. For the purpose, various models including shape models for anatomical objects are intensively and extensively studied so far. In this short paper, we introduce three related topics from our research results, which are (1) uncertainty quantification of anatomical landmarks, (2) non-rigid ICP (iterative closest point) with statistical shape model and outlier consideration, and (3) variational methods for computation of shape average for a 2D point distribution model and a grey-valued image-based shape representation. In each topic, preliminary results are shown by using clinical data sets to prove the clinical feasibility of the proposed models.
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  • Recent Progress on Organ Shape Modeling in Computational Anatomy
    Yoshinobu SATO, Toshiyuki OKADA, Futoshi YOKOTA
    2013 Volume 31 Issue 5 Pages 270-277
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.270
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    Inter-subject variability of multi-organ shapes and their interrelations are modeled and applied to automated segmentation from CT images. In order to deal with large inter-subject variability such as that of the musculoskeletal system and abdomen, hierarchical modeling and conditional modeling methods based on interrelations among organ are introduced. The proposed methods are shown to be effective for segmentation of the musculoskeletal structures of the hip joint including the pelvis, femur, articular cartilage, and muscles, and the upper abdominal organs including the liver, kidneys, spleen, pancreas, gallbladder, aorta, inferior vena cava, and GI-tract. Assuming that automated organ segmentation is possible, the obtained organ shape information is further utilized for statistical modeling of diagnostic and therapeutic decision support. We develop a method for statistical modeling of the disease-specific shape components and show its usefulness in liver fibrosis diagnostic assistance. In addition, we develop methods for statistical modeling of the implants and anatomical structures, which are shown to be effective in automated surgical planning of total hip arthroplasty.
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  • Model Constructions for Computational Anatomy
    Fundamentals and Applications
    Hiroshi FUJITA, Takeshi HARA, Xiangrong ZHOU, Chisako MURAMATSU, Naok ...
    2013 Volume 31 Issue 5 Pages 278-286
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.278
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    This review article describes four parts of our recent progresses in the research which has been performed under the research project “Computational anatomy for computer-aided diagnosis and therapy: Frontiers of medical image sciences” (http://www.comp-anatomy.org/) funded by Grant-in-Aid for Scientific Research on Innovative Areas, MEXT, Japan. The overall purpose of our works under this project is to engage in model constructions for computational anatomy and the applications of the models developed to computer-aided diagnosis (CAD) for automatically recognizing the anatomical structures and analyzing the functions of different organs in a whole body region, all of which are imaged with imaging modalities such as CT, MR, PET, eye fundus photograph, and dental panoramic radiograph. These progresses show the efficiency and potential usefulness of theproposed research works by the promising results.
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  • Computer-Aided Diagnosis Based on Computational Anatomical Models
    Computer-aided Prognosis Prediction of Lung Cancer
    Yoshiki KAWATA, Noboru NIKI
    2013 Volume 31 Issue 5 Pages 287-291
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.287
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    The purpose of this research project is to develop computer-aided diagnosis (CAD) systems that enables early detection and treatment for cancers and lifestyle-related diseases by using multimodal images such as CT, MRI, PET/CT images. The goal of the research and development in this project is to reach a level at which the effectiveness of new CAD technologies can be reasonably proven and clinically tested for ensuring that practical application can be carried out. In this paper, we introduce a topic model to describe intensity distributions inside lung cancers and we investigate its potential usefulness for the computer-aided prognosis prediction of lung cancer.
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  • Fusional Aid for Diagnosis and Therapy Based on Computational Anatomy Model
    Diagnostic and Surgical Aids Based on Anatomical Meta Information
    Kensaku MORI
    2013 Volume 31 Issue 5 Pages 292-298
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.292
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    This paper briefly describes new concept called anatomical meta-information, which is obtained from research outcome of the authors’ research activity in the project of computational anatomy. Also this paper presents new concept of anatomical information circulation from real to virtualized world, from virtualized to real world. These circulation has become possible by medical imaging devices and 3D printing systems. We present recent research outcomes including multi-organ segmentation from abdominal CT volumes, automated annotation of abdominal arteries and veins, anatomical meta-information display, laparoscopic surgery navigation and anatomical meta-information circulation based on a 3D printer.
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  • Computational Anatomy Model for Autopsy Imaging and Its Application to Organ Segmentation
    Akinobu SHIMIZU
    2013 Volume 31 Issue 5 Pages 299-303
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.299
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    This paper presents a computational anatomy model of liver for Autopsy imaging and its application to liver segmentation. First, I describe the synthesis method of pseudo-postmortem liver labels and show the results of the computational anatomy model of liver trained with the pseudo-postmortem liver labels. Then I explain the liver segmentation algorithm from a postmortem CT volume using the model.
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  • Computer-Aided Diagnosis of Computational Anatomical Model and Application of Computer-Aided Autopsy Imaging
    Shoji KIDO, Yasushi HIRANO, Rui XU
    2013 Volume 31 Issue 5 Pages 304-310
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.304
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    In ongoing research project “computational anatomy for computer-aided diagnosis and therapy,” the purpose of research groups in A03 is to perform clinical development of computational anatomy in collaboration with other research groups. Based on this purpose, we A03-1 have carried on our research “computer-aided diagnosis of computational anatomical model and application of computer-aided autopsy imaging.” In the previous research project “multi-organ, multi-disease CAD system,” we have aimed at developing multi-organ, multi-disease CAD systems instead of single-organ single-disease CAD systems. In this current research project, we have intended to perform clinical development of computer-aided diagnosis and therapy systems based on computational anatomy which have high diagnostic accuracy and robustness. Moreover, in adding autopsy imaging which is now socially important to our research subject, we have aimed at constructing CAD system for lifetime images between ante- and post-mortem images. In this article, we describe some current achievements which our research group has performed.
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  • Clinical Application of Navigation Surgery Assisted by the Computational Anatomy
    Morimasa TOMIKAWA, Makoto HASHIZUME
    2013 Volume 31 Issue 5 Pages 311-316
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.311
    JOURNAL FREE ACCESS
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    The results of an open magnetic resonance (MR) imaging-based navigation system for a breast-conserving surgery were presented. This real-time 3D virtual reality navigation system with open MRI is feasible for safe and accurate excision of nonpalpable MRI-detected breast tumors. We have also developed a real-time augmented reality navigation system utilizing the open MRI, and used as many MR-incompatible surgical instruments as possible out of 5G restriction area. Although we used many MR incompatible surgical instruments, 6 laparoscopic operations were completed safely and neither intraoperative complication nor other difficulties occurred. During the operations, the laparoscopic monitor clearly showed the augmented reality models of the intraperitoneal structures such as the common bile duct, the urinary bladder, or the proper position of the prosthesis. Laparoscopic surgery with our developed real-time augmented reality navigation system in the open MRI therapeutic room is so far feasible and effective.
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Work-in-progress
  • GPU Computation of Local Features Derived from 3D-local Statistics
    Tomohiko KIHARA, Hideaki OBATA
    2013 Volume 31 Issue 5 Pages 317-324
    Published: 2013
    Released on J-STAGE: February 01, 2014
    DOIhttps://doi.org/10.11409/mit.31.317
    JOURNAL FREE ACCESS
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    The significant progress of medical imaging devices in recent years has enabled the fast acquisition of high precision volumetric data. Due to this, the need of extension of traditional 2D image processing algorithm to volumetric data has become higher. Because the amount of computational cost increases as the size of 3D data, the application of GPGPU (general-purpose computing on GPUs) based on GPU (graphics processing unit) has been widely researched and several preceding studies were reported. The authors have focused on the improvement of performance and automation for ultra-sound image registration. Instead of using original intensity value, we are currently researching the merit of local features based on 3D local statistics as the similarity measure. As the course of research, we implemented the parallel data processing of local features on GPU and it showed the performance improvement by 19 to 230 times.
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